R Markdown

This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.

When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

uniform_sample <- runif(500, min = 0, max = 10)
summary(uniform_sample)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
## 0.008216 2.429342 4.562907 4.807240 7.319606 9.966002
hist(uniform_sample, main = "Uniform Distribution (0, 10)", xlab = "Values", col = "lightblue")

p_uniform <- punif(4, min = 0, max = 10)
print(p_uniform)
## [1] 0.4
exponential_sample <- rexp(500, rate = 0.1)
summary(exponential_sample)
##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##  0.01092  3.11918  6.83579  9.89172 13.29900 58.68270
hist(exponential_sample, main = "Exponential Distribution (rate = 0.1)", xlab = "Values", col = "lightyellow")

p_exponential <- 1 - pexp(7, rate = 0.1)
print(p_exponential)
## [1] 0.4965853
poisson_sample <- rpois(500, lambda = 3)
summary(poisson_sample)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   2.000   3.000   2.922   4.000   9.000
hist(poisson_sample, main = "Poisson Distribution (lambda = 3)", xlab = "Values", col = "lightpink")

p_poisson <- dpois(4, lambda = 3)
print(p_poisson)
## [1] 0.1680314
binomial_sample <- rbinom(500, size = 10, prob = 0.5)
summary(binomial_sample)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   4.000   5.000   4.976   6.000  10.000
hist(binomial_sample, main = "Binomial Distribution (n = 10, p = 0.5)", xlab = "Values", col = "lightgreen")

p_binomial <- dbinom(3, size = 10, prob = 0.5)
print(p_binomial)
## [1] 0.1171875